{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import SALib\n",
    "import numpy\n",
    "from SALib.sample import saltelli\n",
    "from SALib.sample import fast_sampler\n",
    "from SALib.test_functions import Ishigami\n",
    "from SALib.analyze import sobol\n",
    "from SALib.analyze import fast\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "### Sample code to test SALib functionality"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          ST   ST_conf\n",
      "x1  0.555860  0.089216\n",
      "x2  0.441898  0.037515\n",
      "x3  0.244675  0.029756\n",
      "          S1   S1_conf\n",
      "x1  0.316832  0.057668\n",
      "x2  0.443763  0.049940\n",
      "x3  0.012203  0.053409\n",
      "                S2   S2_conf\n",
      "(x1, x2)  0.009254  0.088655\n",
      "(x1, x3)  0.238172  0.120701\n",
      "(x2, x3) -0.004888  0.071319\n",
      "[0.31683154 0.44376306 0.01220312]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lab\\AppData\\Local\\Temp\\ipykernel_42360\\677680878.py:11: DeprecationWarning: `salib.sample.saltelli` will be removed in SALib 1.5. Please use `salib.sample.sobol`\n",
      "  param_values = saltelli.sample(problem, 1024)\n"
     ]
    }
   ],
   "source": [
    "# Define the model inputs\n",
    "problem = {\n",
    "    'num_vars': 3,\n",
    "    'names': ['x1', 'x2', 'x3'],\n",
    "    'bounds': [[-3.14159265359, 3.14159265359],\n",
    "               [-3.14159265359, 3.14159265359],\n",
    "               [-3.14159265359, 3.14159265359]]\n",
    "}\n",
    "\n",
    "# Generate samples\n",
    "param_values = saltelli.sample(problem, 1024)\n",
    "\n",
    "# Run model (example)\n",
    "Y = Ishigami.evaluate(param_values)\n",
    "\n",
    "# Perform analysis\n",
    "Si = sobol.analyze(problem, Y, print_to_console=True)\n",
    "\n",
    "# Print the first-order sensitivity indices\n",
    "print(Si['S1'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## List of steps:\n",
    " 1. Generate a list of parameters, using\n",
    " `SALib.sample.fast_sampler.sample(problem, N, M=4, seed=None)`\n",
    "2. Run the simulation with that matrix of parameters\n",
    "3. Give the time to half max and slope of area losses that result to\n",
    "`SALib.analyze.fast.analyze(problem, Y, M=4, num_resamples=100, conf_level=0.95, print_to_console=False, seed=None)`"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "# Define the problem with parameter ranges\n",
    "problem = {\n",
    "    'num_vars': 8,\n",
    "    'names': ['rest', 'amp', 'freq', 'pack', 'visc', 'rest_n', 'amp_n', 'freq_n'],\n",
    "    'bounds': [ [0.6,1.4],\n",
    "               [0.05,0.33],\n",
    "               [0.6,1.0],\n",
    "               [0.7,1.5],\n",
    "               [5,25],\n",
    "               [0.6,1.4],\n",
    "               [0.05,0.33],\n",
    "               [0.6,1.0] ]\n",
    "}"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1.20014924, 0.26005223, 0.90007462, ..., 1.20014924, 0.26005223,\n        0.90007462],\n       [1.00814924, 0.25557223, 0.89367462, ..., 1.18734924, 0.25557223,\n        0.89367462],\n       [0.81614924, 0.25109223, 0.88727462, ..., 1.17454924, 0.25109223,\n        0.88727462],\n       ...,\n       [0.99727912, 0.18904769, 0.79863956, ..., 0.99727912, 0.18904769,\n        0.93256044],\n       [0.98447912, 0.18456769, 0.79223956, ..., 0.98447912, 0.18456769,\n        0.97143956],\n       [0.97167912, 0.18008769, 0.78583956, ..., 0.97167912, 0.18008769,\n        0.87543956]])"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate parameters to use for fast analysis\n",
    "params = SALib.sample.fast_sampler.sample(problem, N=125)\n",
    "params"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [],
   "source": [
    "np.savetxt(\"fast_params.csv\",params, delimiter=',')"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "array([41.278 , 31.923 , 15.521 , 24.079 , 41.781 , 49.457 , 66.89  ,\n       55.837 , 51.722 , 45.474 , 26.459 , 28.822 , 35.155 , 56.773 ,\n       69.862 , 60.593 , 39.766 , 44.521 , 32.759 , 34.554 , 44.606 ,\n       59.538 , 63.915 , 65.869 , 56.686 , 44.572 , 25.544 , 27.242 ,\n       36.502 , 53.52  , 56.442 , 61.607 , 43.194 , 37.249 , 29.905 ,\n       23.792 , 30.602 , 40.5   , 41.803 , 49.253 , 22.656 , 28.171 ,\n       15.526 , 14.683 , 25.292 , 27.14  , 30.2   , 58.366 , 30.419 ,\n       39.021 , 16.134 ,  3.8455, 20.271 , 25.612 , 44.167 , 43.349 ,\n       33.834 , 33.3   , 23.596 , 16.89  , 28.522 , 30.06  , 45.194 ,\n       70.924 , 53.923 , 33.969 , 20.128 , 27.324 , 19.59  , 25.157 ,\n       31.759 , 36.659 , 34.813 , 38.172 , 46.706 , 41.522 , 30.212 ,\n       38.089 , 46.167 , 43.156 , 29.678 , 44.821 , 51.05  , 33.469 ,\n       47.253 , 22.125 , 37.157 , 48.679 , 36.5   , 37.66  , 39.637 ,\n       51.479 , 44.852 , 40.698 , 51.684 , 55.104 , 49.58  , 43.57  ,\n       60.403 , 30.165 , 47.466 , 37.325 , 36.943 , 56.084 , 19.887 ,\n       44.875 , 48.268 , 45.314 , 40.804 , 31.581 , 34.345 , 29.742 ,\n       36.941 , 41.    , 42.018 , 25.551 , 24.384 , 23.934 , 19.441 ,\n       23.747 , 15.948 , 33.845 , 31.81  , 34.286 , 14.702 ,  7.9277,\n       10.598 , 18.965 , 25.752 , 30.435 , 33.754 , 27.264 , 27.93  ,\n       29.57  , 30.126 , 33.648 , 22.45  , 34.232 , 35.925 , 35.824 ,\n       34.994 , 37.58  , 36.039 , 44.652 , 32.299 , 36.    , 30.952 ,\n       31.943 , 32.044 , 33.219 , 47.617 , 31.689 , 35.455 , 29.167 ,\n       16.681 , 33.718 , 37.569 , 23.672 , 38.749 , 52.045 , 19.97  ,\n       51.967 , 37.697 , 25.195 ,  6.7277, 21.437 , 36.471 , 37.636 ,\n       42.993 , 36.201 , 34.106 , 29.041 , 47.049 , 36.75  , 45.945 ,\n       37.811 , 28.51  , 32.512 , 35.179 , 22.536 , 42.994 , 28.461 ,\n       42.478 , 37.314 , 36.975 , 35.241 , 33.687 , 30.68  , 39.615 ,\n       24.627 , 28.407 , 30.852 , 33.805 , 27.363 , 28.483 , 47.354 ,\n       36.014 , 39.663 , 41.765 , 23.009 , 39.795 , 27.046 , 39.097 ,\n       37.035 , 35.542 , 31.758 , 29.443 , 35.078 , 29.008 , 31.5   ,\n       25.218 , 24.874 , 11.97  , 28.707 , 27.682 , 29.332 , 16.026 ,\n       17.939 , 14.54  , 31.217 , 37.281 , 36.844 , 36.046 , 32.51  ,\n       27.228 , 34.952 , 32.    , 25.844 , 29.742 , 36.216 , 47.22  ,\n       43.113 , 46.24  , 35.242 , 38.333 , 35.752 , 51.294 , 54.014 ,\n       58.761 , 44.29  , 51.893 , 45.757 , 11.747 , 50.841 , 55.057 ,\n       61.032 , 63.903 , 52.947 , 53.321 , 54.024 , 52.373 , 45.027 ,\n       55.192 , 47.927 , 50.45  , 52.242 , 56.142 , 51.333 , 25.116 ,\n       39.314 , 38.858 , 48.152 , 53.407 , 44.991 , 59.447 , 62.548 ,\n       59.155 , 44.681 , 48.891 , 53.592 , 47.281 , 44.942 , 49.885 ,\n       45.329 , 59.261 , 50.629 , 29.583 , 32.802 , 32.6   , 33.303 ,\n       29.445 , 45.74  , 38.781 , 30.576 , 35.763 , 26.404 , 39.075 ,\n       28.64  , 31.104 , 29.905 , 35.442 , 26.807 , 32.952 , 19.216 ,\n       30.607 , 31.541 ,  9.952 , 32.037 , 29.326 , 29.962 , 31.555 ,\n       33.268 , 30.687 , 33.245 , 37.06  , 27.652 , 34.285 , 32.069 ,\n       42.875 , 39.658 , 40.326 , 42.871 , 38.365 , 42.56  , 35.922 ,\n       42.855 , 31.242 , 35.801 , 51.278 , 40.884 , 46.824 , 49.114 ,\n       47.209 , 60.233 , 43.656 , 30.567 , 22.771 , 31.219 , 32.324 ,\n       30.507 , 16.406 , 24.547 , 31.271 , 33.518 , 25.614 , 29.612 ,\n       25.745 , 32.719 , 30.34  , 38.25  , 41.161 , 35.333 , 37.346 ,\n       41.18  , 28.798 , 36.09  , 46.497 , 46.167 , 33.575 , 46.809 ,\n       31.4   , 36.294 , 40.296 , 47.097 , 48.592 , 41.741 , 49.605 ,\n       38.625 , 54.199 , 55.732 , 57.015 , 50.945 , 39.892 , 46.77  ,\n       29.099 , 48.046 , 49.885 , 49.406 , 58.213 , 50.881 , 60.565 ,\n       52.041 , 47.873 , 45.119 , 43.798 , 34.682 , 47.141 , 37.037 ,\n       40.696 , 37.788 , 41.41  , 39.613 , 38.213 , 22.261 , 34.075 ,\n       35.405 , 30.637 , 34.682 , 32.725 , 32.187 , 52.293 , 31.161 ,\n       37.5   , 29.928 , 36.828 , 41.592 , 40.917 , 39.423 , 39.859 ,\n       45.222 , 35.825 , 33.953 , 39.405 , 27.196 , 42.111 , 39.503 ,\n       36.744 , 30.276 , 37.886 , 33.624 , 31.961 , 34.481 , 36.911 ,\n       30.019 , 46.388 , 31.34  , 34.518 , 28.852 , 28.325 , 28.603 ,\n       30.607 , 34.552 , 43.149 , 31.606 , 22.65  , 34.538 , 28.028 ,\n       33.144 , 28.795 , 32.827 , 39.482 , 38.382 , 22.835 , 32.826 ,\n       46.198 , 32.294 , 39.569 , 39.705 , 41.24  , 26.482 , 48.083 ,\n       48.356 , 19.863 , 24.851 , 46.882 , 55.553 , 59.469 , 51.665 ,\n       33.33  , 18.41  , 47.107 , 30.468 , 43.468 , 40.036 , 31.204 ,\n       45.757 , 34.375 , 46.705 , 32.243 , 13.534 , 17.445 , 45.807 ,\n       48.649 , 22.9   , 48.014 , 40.193 , 18.    , 17.064 , 35.284 ,\n       58.068 , 23.382 , 42.533 , 28.7   , 34.042 , 35.201 , 31.171 ,\n       36.897 , 32.776 , 41.771 , 33.871 , 34.568 , 37.899 , 21.124 ,\n       38.473 , 29.314 , 27.625 , 23.051 , 30.699 , 33.052 , 35.295 ,\n       30.05  , 28.837 , 37.501 , 34.083 , 32.767 , 30.965 , 32.926 ,\n       27.224 , 29.25  , 27.605 ])"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in half times generated using parameters above\n",
    "resultsH = np.loadtxt(\"----\", delimiter=',', dtype=float)\n",
    "resultsH"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "array([41.278 , 31.923 , 15.521 , 24.079 , 41.781 , 49.457 , 66.89  ,\n       55.837 , 51.722 , 45.474 , 26.459 , 28.822 , 35.155 , 56.773 ,\n       69.862 , 60.593 , 39.766 , 44.521 , 32.759 , 34.554 , 44.606 ,\n       59.538 , 63.915 , 65.869 , 56.686 , 44.572 , 25.544 , 27.242 ,\n       36.502 , 53.52  , 56.442 , 61.607 , 43.194 , 37.249 , 29.905 ,\n       23.792 , 30.602 , 40.5   , 41.803 , 49.253 , 22.656 , 28.171 ,\n       15.526 , 14.683 , 25.292 , 27.14  , 30.2   , 58.366 , 30.419 ,\n       39.021 , 16.134 ,  3.8455, 20.271 , 25.612 , 44.167 , 43.349 ,\n       33.834 , 33.3   , 23.596 , 16.89  , 28.522 , 30.06  , 45.194 ,\n       70.924 , 53.923 , 33.969 , 20.128 , 27.324 , 19.59  , 25.157 ,\n       31.759 , 36.659 , 34.813 , 38.172 , 46.706 , 41.522 , 30.212 ,\n       38.089 , 46.167 , 43.156 , 29.678 , 44.821 , 51.05  , 33.469 ,\n       47.253 , 22.125 , 37.157 , 48.679 , 36.5   , 37.66  , 39.637 ,\n       51.479 , 44.852 , 40.698 , 51.684 , 55.104 , 49.58  , 43.57  ,\n       60.403 , 30.165 , 47.466 , 37.325 , 36.943 , 56.084 , 19.887 ,\n       44.875 , 48.268 , 45.314 , 40.804 , 31.581 , 34.345 , 29.742 ,\n       36.941 , 41.    , 42.018 , 25.551 , 24.384 , 23.934 , 19.441 ,\n       23.747 , 15.948 , 33.845 , 31.81  , 34.286 , 14.702 ,  7.9277,\n       10.598 , 18.965 , 25.752 , 30.435 , 33.754 , 27.264 , 27.93  ,\n       29.57  , 30.126 , 33.648 , 22.45  , 34.232 , 35.925 , 35.824 ,\n       34.994 , 37.58  , 36.039 , 44.652 , 32.299 , 36.    , 30.952 ,\n       31.943 , 32.044 , 33.219 , 47.617 , 31.689 , 35.455 , 29.167 ,\n       16.681 , 33.718 , 37.569 , 23.672 , 38.749 , 52.045 , 19.97  ,\n       51.967 , 37.697 , 25.195 ,  6.7277, 21.437 , 36.471 , 37.636 ,\n       42.993 , 36.201 , 34.106 , 29.041 , 47.049 , 36.75  , 45.945 ,\n       37.811 , 28.51  , 32.512 , 35.179 , 22.536 , 42.994 , 28.461 ,\n       42.478 , 37.314 , 36.975 , 35.241 , 33.687 , 30.68  , 39.615 ,\n       24.627 , 28.407 , 30.852 , 33.805 , 27.363 , 28.483 , 47.354 ,\n       36.014 , 39.663 , 41.765 , 23.009 , 39.795 , 27.046 , 39.097 ,\n       37.035 , 35.542 , 31.758 , 29.443 , 35.078 , 29.008 , 31.5   ,\n       25.218 , 24.874 , 11.97  , 28.707 , 27.682 , 29.332 , 16.026 ,\n       17.939 , 14.54  , 31.217 , 37.281 , 36.844 , 36.046 , 32.51  ,\n       27.228 , 34.952 , 32.    , 25.844 , 29.742 , 36.216 , 47.22  ,\n       43.113 , 46.24  , 35.242 , 38.333 , 35.752 , 51.294 , 54.014 ,\n       58.761 , 44.29  , 51.893 , 45.757 , 11.747 , 50.841 , 55.057 ,\n       61.032 , 63.903 , 52.947 , 53.321 , 54.024 , 52.373 , 45.027 ,\n       55.192 , 47.927 , 50.45  , 52.242 , 56.142 , 51.333 , 25.116 ,\n       39.314 , 38.858 , 48.152 , 53.407 , 44.991 , 59.447 , 62.548 ,\n       59.155 , 44.681 , 48.891 , 53.592 , 47.281 , 44.942 , 49.885 ,\n       45.329 , 59.261 , 50.629 , 29.583 , 32.802 , 32.6   , 33.303 ,\n       29.445 , 45.74  , 38.781 , 30.576 , 35.763 , 26.404 , 39.075 ,\n       28.64  , 31.104 , 29.905 , 35.442 , 26.807 , 32.952 , 19.216 ,\n       30.607 , 31.541 ,  9.952 , 32.037 , 29.326 , 29.962 , 31.555 ,\n       33.268 , 30.687 , 33.245 , 37.06  , 27.652 , 34.285 , 32.069 ,\n       42.875 , 39.658 , 40.326 , 42.871 , 38.365 , 42.56  , 35.922 ,\n       42.855 , 31.242 , 35.801 , 51.278 , 40.884 , 46.824 , 49.114 ,\n       47.209 , 60.233 , 43.656 , 30.567 , 22.771 , 31.219 , 32.324 ,\n       30.507 , 16.406 , 24.547 , 31.271 , 33.518 , 25.614 , 29.612 ,\n       25.745 , 32.719 , 30.34  , 38.25  , 41.161 , 35.333 , 37.346 ,\n       41.18  , 28.798 , 36.09  , 46.497 , 46.167 , 33.575 , 46.809 ,\n       31.4   , 36.294 , 40.296 , 47.097 , 48.592 , 41.741 , 49.605 ,\n       38.625 , 54.199 , 55.732 , 57.015 , 50.945 , 39.892 , 46.77  ,\n       29.099 , 48.046 , 49.885 , 49.406 , 58.213 , 50.881 , 60.565 ,\n       52.041 , 47.873 , 45.119 , 43.798 , 34.682 , 47.141 , 37.037 ,\n       40.696 , 37.788 , 41.41  , 39.613 , 38.213 , 22.261 , 34.075 ,\n       35.405 , 30.637 , 34.682 , 32.725 , 32.187 , 52.293 , 31.161 ,\n       37.5   , 29.928 , 36.828 , 41.592 , 40.917 , 39.423 , 39.859 ,\n       45.222 , 35.825 , 33.953 , 39.405 , 27.196 , 42.111 , 39.503 ,\n       36.744 , 30.276 , 37.886 , 33.624 , 31.961 , 34.481 , 36.911 ,\n       30.019 , 46.388 , 31.34  , 34.518 , 28.852 , 28.325 , 28.603 ,\n       30.607 , 34.552 , 43.149 , 31.606 , 22.65  , 34.538 , 28.028 ,\n       33.144 , 28.795 , 32.827 , 39.482 , 38.382 , 22.835 , 32.826 ,\n       46.198 , 32.294 , 39.569 , 39.705 , 41.24  , 26.482 , 48.083 ,\n       48.356 , 19.863 , 24.851 , 46.882 , 55.553 , 59.469 , 51.665 ,\n       33.33  , 18.41  , 47.107 , 30.468 , 43.468 , 40.036 , 31.204 ,\n       45.757 , 34.375 , 46.705 , 32.243 , 13.534 , 17.445 , 45.807 ,\n       48.649 , 22.9   , 48.014 , 40.193 , 18.    , 17.064 , 35.284 ,\n       58.068 , 23.382 , 42.533 , 28.7   , 34.042 , 35.201 , 31.171 ,\n       36.897 , 32.776 , 41.771 , 33.871 , 34.568 , 37.899 , 21.124 ,\n       38.473 , 29.314 , 27.625 , 23.051 , 30.699 , 33.052 , 35.295 ,\n       30.05  , 28.837 , 37.501 , 34.083 , 32.767 , 30.965 , 32.926 ,\n       27.224 , 29.25  , 27.605 ])"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean data, remove negative, NaN, and -inf values\n",
    "resultsH = resultsH[~np.isnan(resultsH)]\n",
    "resultsH = resultsH[~np.isneginf(resultsH)]\n",
    "resultsH = resultsH[resultsH>0]\n",
    "resultsH"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              S1        ST   S1_conf   ST_conf\n",
      "rest    0.344395  0.705872  0.223647  0.136511\n",
      "amp     0.128915  0.814153  0.209541  0.134391\n",
      "freq    0.069122  0.738005  0.232180  0.108799\n",
      "pack    0.118395  0.494478  0.207822  0.134062\n",
      "visc    0.138874  0.411996  0.202344  0.115960\n",
      "rest_n  0.058513  0.575268  0.238477  0.107495\n",
      "amp_n   0.095304  0.530003  0.232763  0.149266\n",
      "freq_n  0.140926  0.919039  0.222601  0.127396\n"
     ]
    }
   ],
   "source": [
    "# Run sensitivity analysis for half time\n",
    "# Reduce to a multiple of 8 for analysis\n",
    "SiH = SALib.analyze.fast.analyze(problem, resultsH[0:480], print_to_console=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SiH.plot()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'S2'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_42360\\2470374404.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mprint\u001B[0m\u001B[1;33m(\u001B[0m\u001B[0mSiH\u001B[0m\u001B[1;33m[\u001B[0m\u001B[1;34m'S2'\u001B[0m\u001B[1;33m]\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m",
      "\u001B[1;31mKeyError\u001B[0m: 'S2'"
     ]
    }
   ],
   "source": [
    "print(SiH['S2'])"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "array([-0.019933 , -0.023183 , -0.041986 , -0.01913  , -0.022406 ,\n       -0.014604 , -0.021342 , -0.011413 , -0.019497 , -0.018269 ,\n       -0.01677  , -0.017048 , -0.010998 , -0.021128 , -0.01539  ,\n       -0.0095009, -0.00946  , -0.026632 , -0.023977 , -0.025486 ,\n       -0.013561 , -0.019924 , -0.010111 , -0.013125 , -0.017809 ,\n       -0.023484 , -0.014391 , -0.026291 , -0.019438 , -0.018256 ,\n       -0.014357 , -0.012585 , -0.015175 , -0.029536 , -0.023434 ,\n       -0.030919 , -0.02599  , -0.01302  , -0.011121 , -0.010398 ,\n       -0.010872 , -0.02728  , -0.015651 , -0.023066 , -0.020714 ,\n       -0.010464 , -0.010004 , -0.014775 , -0.047989 , -0.027307 ,\n       -0.01527  , -0.17572  , -0.018337 , -0.011014 , -0.02166  ,\n       -0.01012  , -0.012288 , -0.016625 , -0.02613  , -0.026839 ,\n       -0.037187 , -0.024599 , -0.023231 , -0.026346 , -0.017864 ,\n       -0.024082 , -0.011802 , -0.020635 , -0.017441 , -0.025063 ,\n       -0.020658 , -0.025539 , -0.012924 , -0.013386 ,  0.06052  ,\n       -0.022861 , -0.016115 , -0.025483 , -0.016771 , -0.013434 ,\n       -0.023327 , -0.018158 , -0.015901 , -0.010261 , -0.015581 ,\n       -0.0051286, -0.01003  , -0.018886 , -0.00904  , -0.026094 ,\n       -0.031942 , -0.013678 , -0.009555 , -0.014212 , -0.013547 ,\n       -0.011638 , -0.032711 , -0.0064814, -0.016117 , -0.0054533,\n       -0.011944 , -0.025236 , -0.01096  , -0.044243 , -0.022921 ,\n       -0.007745 , -0.010723 , -0.019636 , -0.021103 , -0.016607 ,\n       -0.016078 , -0.024245 , -0.020935 , -0.021739 , -0.024036 ,\n       -0.018092 , -0.013944 , -0.019909 , -0.019157 , -0.021536 ,\n       -0.017457 , -0.020286 , -0.037491 , -0.0045686, -0.019844 ,\n       -0.072174 , -0.020414 , -0.023299 , -0.020446 , -0.021469 ,\n       -0.005473 , -0.022668 , -0.017148 , -0.025339 , -0.018184 ,\n       -0.022589 , -0.017526 , -0.02419  , -0.017931 , -0.023548 ,\n       -0.019688 , -0.015607 , -0.036342 , -0.025974 , -0.012407 ,\n       -0.016082 , -0.003773 , -0.012742 , -0.015536 , -0.010145 ,\n       -0.013577 , -0.012327 , -0.014102 , -0.015114 , -0.010187 ,\n       -0.029834 , -0.0088267, -0.025401 , -0.0098314, -0.017899 ,\n       -0.026516 , -0.00903  , -0.022658 , -0.024759 , -0.087295 ,\n       -0.0031571, -0.013056 , -0.011149 , -0.00745  , -0.006149 ,\n       -0.020506 , -0.012321 , -0.032393 , -0.013604 , -0.012611 ,\n       -0.015657 , -0.021659 , -0.01397  , -0.009011 , -0.01507  ,\n       -0.018854 , -0.012819 , -0.023325 , -0.021579 , -0.0091286,\n       -0.017278 , -0.016796 , -0.013773 , -0.023297 , -0.013421 ,\n       -0.024619 , -0.015855 , -0.022471 , -0.017288 , -0.024022 ,\n       -0.030841 , -0.022329 , -0.023264 , -0.028763 , -0.00929  ,\n       -0.026798 , -0.01597  , -0.027279 , -0.021401 , -0.020509 ,\n       -0.011963 , -0.016422 , -0.026902 , -0.016379 , -0.017678 ,\n       -0.018366 , -0.01562  , -0.045581 , -0.028876 , -0.020863 ,\n       -0.020687 , -0.033277 , -0.016048 , -0.022013 , -0.021013 ,\n       -0.027513 , -0.027037 , -0.020249 , -0.017055 , -0.015228 ,\n       -0.02164  , -0.015374 , -0.0098829, -0.012075 , -0.01039  ,\n       -0.018661 , -0.014769 , -0.017273 , -0.012294 , -0.00937  ,\n       -0.013781 , -0.021954 , -0.020499 , -0.027476 , -0.019266 ,\n       -0.014719 , -0.014337 , -0.041529 , -0.012872 , -0.031947 ,\n       -0.021468 , -0.014754 , -0.012923 , -0.013877 , -0.017977 ,\n       -0.011463 , -0.01477  , -0.020288 , -0.015249 , -0.015799 ,\n       -0.01985  , -0.025356 , -0.023231 , -0.016788 , -0.024007 ,\n       -0.01239  , -0.015493 , -0.019162 , -0.012856 , -0.016282 ,\n       -0.027242 , -0.017868 , -0.011043 , -0.011289 , -0.016504 ,\n       -0.013822 , -0.011418 , -0.015193 , -0.014    , -0.026316 ,\n       -0.014145 , -0.0078914, -0.0092288, -0.024544 , -0.01063  ,\n       -0.013708 , -0.027271 , -0.017877 , -0.015848 , -0.0193   ,\n       -0.025231 , -0.018634 , -0.013794 , -0.016119 , -0.01876  ,\n       -0.02228  , -0.013267 , -0.020171 , -0.01199  , -0.017314 ,\n       -0.022533 , -0.055853 , -0.0080033, -0.020894 , -0.024762 ,\n       -0.027579 , -0.019569 , -0.019207 , -0.014884 , -0.027187 ,\n       -0.014515 , -0.019781 , -0.023587 , -0.021531 , -0.019929 ,\n       -0.016825 , -0.021648 , -0.014026 , -0.020342 , -0.022039 ,\n       -0.01436  , -0.013057 , -0.014439 , -0.029291 , -0.012994 ,\n       -0.015969 , -0.021413 , -0.017189 , -0.045197 , -0.014313 ,\n       -0.016358 , -0.013815 , -0.025778 , -0.023828 , -0.010943 ,\n       -0.026326 , -0.014805 , -0.027022 , -0.018405 , -0.020536 ,\n       -0.024295 , -0.025872 , -0.019109 , -0.015074 , -0.024606 ,\n       -0.020043 , -0.020068 , -0.017542 , -0.020386 , -0.014963 ,\n       -0.012179 , -0.024656 , -0.024885 , -0.026767 , -0.017213 ,\n       -0.0068675, -0.026001 , -0.012821 , -0.014043 , -0.015499 ,\n       -0.016053 , -0.01553  , -0.0072733, -0.016908 , -0.016055 ,\n       -0.031109 , -0.0096055, -0.00881  , -0.016397 , -0.015503 ,\n       -0.012562 , -0.012927 , -0.015667 , -0.023282 , -0.01426  ,\n       -0.031261 , -0.01445  , -0.019086 , -0.017776 , -0.018951 ,\n       -0.019193 , -0.024323 , -0.021811 , -0.028815 , -0.013792 ,\n       -0.025327 , -0.021204 , -0.014669 , -0.024293 , -0.016869 ,\n       -0.019921 , -0.017325 , -0.022056 , -0.019006 , -0.015761 ,\n       -0.019247 , -0.0069429, -0.014687 , -0.0067833, -0.017085 ,\n       -0.0094571, -0.012888 , -0.013054 , -0.010311 , -0.014617 ,\n       -0.024905 , -0.012157 , -0.016666 , -0.010913 , -0.018628 ,\n       -0.018911 , -0.015936 , -0.033855 , -0.02337  , -0.016726 ,\n       -0.0141   , -0.020383 , -0.018098 , -0.019994 , -0.0097567,\n       -0.026774 , -0.04357  , -0.017097 , -0.019039 , -0.019237 ,\n       -0.021547 , -0.023871 , -0.023247 , -0.018103 , -0.012049 ,\n       -0.021811 , -0.011139 , -0.017744 , -0.01123  , -0.014468 ,\n       -0.016065 , -0.016028 , -0.019477 , -0.014146 , -0.018802 ,\n       -0.00921  , -0.013541 , -0.014684 , -0.006355 , -0.009856 ,\n       -0.018237 , -0.019603 , -0.021236 , -0.011943 , -0.014418 ,\n       -0.020925 , -0.027817 , -0.017508 , -0.021509 , -0.00953  ,\n       -0.0152   , -0.021263 , -0.01017  , -0.0065271, -0.032973 ,\n       -0.013707 , -0.022841 , -0.014494 , -0.024446 , -0.023226 ,\n       -0.026063 , -0.028042 , -0.018809 , -0.040551 , -0.0066864,\n       -0.030315 , -0.013748 , -0.023841 , -0.01677  , -0.025636 ,\n       -0.027406 , -0.024676 , -0.020194 , -0.023646 , -0.01435  ,\n       -0.013478 , -0.021398 , -0.0035517, -0.017634 , -0.017357 ,\n       -0.016666 , -0.008415 , -0.027327 , -0.015674 , -0.012839 ,\n       -0.012711 , -0.014075 , -0.016813 , -0.031901 , -0.01976  ,\n       -0.014225 , -0.019148 , -0.0035523, -0.022869 , -0.016685 ,\n       -0.01308  , -0.0087338, -0.017738 , -0.025458 , -0.01749  ])"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Read in slopes generated using parameters above\n",
    "resultsS = np.loadtxt(\"-----\", delimiter=',', dtype=float)\n",
    "resultsS"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "array([-0.019933 , -0.023183 , -0.041986 , -0.01913  , -0.022406 ,\n       -0.014604 , -0.021342 , -0.011413 , -0.019497 , -0.018269 ,\n       -0.01677  , -0.017048 , -0.010998 , -0.021128 , -0.01539  ,\n       -0.0095009, -0.00946  , -0.026632 , -0.023977 , -0.025486 ,\n       -0.013561 , -0.019924 , -0.010111 , -0.013125 , -0.017809 ,\n       -0.023484 , -0.014391 , -0.026291 , -0.019438 , -0.018256 ,\n       -0.014357 , -0.012585 , -0.015175 , -0.029536 , -0.023434 ,\n       -0.030919 , -0.02599  , -0.01302  , -0.011121 , -0.010398 ,\n       -0.010872 , -0.02728  , -0.015651 , -0.023066 , -0.020714 ,\n       -0.010464 , -0.010004 , -0.014775 , -0.047989 , -0.027307 ,\n       -0.01527  , -0.17572  , -0.018337 , -0.011014 , -0.02166  ,\n       -0.01012  , -0.012288 , -0.016625 , -0.02613  , -0.026839 ,\n       -0.037187 , -0.024599 , -0.023231 , -0.026346 , -0.017864 ,\n       -0.024082 , -0.011802 , -0.020635 , -0.017441 , -0.025063 ,\n       -0.020658 , -0.025539 , -0.012924 , -0.013386 , -0.022861 ,\n       -0.016115 , -0.025483 , -0.016771 , -0.013434 , -0.023327 ,\n       -0.018158 , -0.015901 , -0.010261 , -0.015581 , -0.0051286,\n       -0.01003  , -0.018886 , -0.00904  , -0.026094 , -0.031942 ,\n       -0.013678 , -0.009555 , -0.014212 , -0.013547 , -0.011638 ,\n       -0.032711 , -0.0064814, -0.016117 , -0.0054533, -0.011944 ,\n       -0.025236 , -0.01096  , -0.044243 , -0.022921 , -0.007745 ,\n       -0.010723 , -0.019636 , -0.021103 , -0.016607 , -0.016078 ,\n       -0.024245 , -0.020935 , -0.021739 , -0.024036 , -0.018092 ,\n       -0.013944 , -0.019909 , -0.019157 , -0.021536 , -0.017457 ,\n       -0.020286 , -0.037491 , -0.0045686, -0.019844 , -0.072174 ,\n       -0.020414 , -0.023299 , -0.020446 , -0.021469 , -0.005473 ,\n       -0.022668 , -0.017148 , -0.025339 , -0.018184 , -0.022589 ,\n       -0.017526 , -0.02419  , -0.017931 , -0.023548 , -0.019688 ,\n       -0.015607 , -0.036342 , -0.025974 , -0.012407 , -0.016082 ,\n       -0.003773 , -0.012742 , -0.015536 , -0.010145 , -0.013577 ,\n       -0.012327 , -0.014102 , -0.015114 , -0.010187 , -0.029834 ,\n       -0.0088267, -0.025401 , -0.0098314, -0.017899 , -0.026516 ,\n       -0.00903  , -0.022658 , -0.024759 , -0.087295 , -0.0031571,\n       -0.013056 , -0.011149 , -0.00745  , -0.006149 , -0.020506 ,\n       -0.012321 , -0.032393 , -0.013604 , -0.012611 , -0.015657 ,\n       -0.021659 , -0.01397  , -0.009011 , -0.01507  , -0.018854 ,\n       -0.012819 , -0.023325 , -0.021579 , -0.0091286, -0.017278 ,\n       -0.016796 , -0.013773 , -0.023297 , -0.013421 , -0.024619 ,\n       -0.015855 , -0.022471 , -0.017288 , -0.024022 , -0.030841 ,\n       -0.022329 , -0.023264 , -0.028763 , -0.00929  , -0.026798 ,\n       -0.01597  , -0.027279 , -0.021401 , -0.020509 , -0.011963 ,\n       -0.016422 , -0.026902 , -0.016379 , -0.017678 , -0.018366 ,\n       -0.01562  , -0.045581 , -0.028876 , -0.020863 , -0.020687 ,\n       -0.033277 , -0.016048 , -0.022013 , -0.021013 , -0.027513 ,\n       -0.027037 , -0.020249 , -0.017055 , -0.015228 , -0.02164  ,\n       -0.015374 , -0.0098829, -0.012075 , -0.01039  , -0.018661 ,\n       -0.014769 , -0.017273 , -0.012294 , -0.00937  , -0.013781 ,\n       -0.021954 , -0.020499 , -0.027476 , -0.019266 , -0.014719 ,\n       -0.014337 , -0.041529 , -0.012872 , -0.031947 , -0.021468 ,\n       -0.014754 , -0.012923 , -0.013877 , -0.017977 , -0.011463 ,\n       -0.01477  , -0.020288 , -0.015249 , -0.015799 , -0.01985  ,\n       -0.025356 , -0.023231 , -0.016788 , -0.024007 , -0.01239  ,\n       -0.015493 , -0.019162 , -0.012856 , -0.016282 , -0.027242 ,\n       -0.017868 , -0.011043 , -0.011289 , -0.016504 , -0.013822 ,\n       -0.011418 , -0.015193 , -0.014    , -0.026316 , -0.014145 ,\n       -0.0078914, -0.0092288, -0.024544 , -0.01063  , -0.013708 ,\n       -0.027271 , -0.017877 , -0.015848 , -0.0193   , -0.025231 ,\n       -0.018634 , -0.013794 , -0.016119 , -0.01876  , -0.02228  ,\n       -0.013267 , -0.020171 , -0.01199  , -0.017314 , -0.022533 ,\n       -0.055853 , -0.0080033, -0.020894 , -0.024762 , -0.027579 ,\n       -0.019569 , -0.019207 , -0.014884 , -0.027187 , -0.014515 ,\n       -0.019781 , -0.023587 , -0.021531 , -0.019929 , -0.016825 ,\n       -0.021648 , -0.014026 , -0.020342 , -0.022039 , -0.01436  ,\n       -0.013057 , -0.014439 , -0.029291 , -0.012994 , -0.015969 ,\n       -0.021413 , -0.017189 , -0.045197 , -0.014313 , -0.016358 ,\n       -0.013815 , -0.025778 , -0.023828 , -0.010943 , -0.026326 ,\n       -0.014805 , -0.027022 , -0.018405 , -0.020536 , -0.024295 ,\n       -0.025872 , -0.019109 , -0.015074 , -0.024606 , -0.020043 ,\n       -0.020068 , -0.017542 , -0.020386 , -0.014963 , -0.012179 ,\n       -0.024656 , -0.024885 , -0.026767 , -0.017213 , -0.0068675,\n       -0.026001 , -0.012821 , -0.014043 , -0.015499 , -0.016053 ,\n       -0.01553  , -0.0072733, -0.016908 , -0.016055 , -0.031109 ,\n       -0.0096055, -0.00881  , -0.016397 , -0.015503 , -0.012562 ,\n       -0.012927 , -0.015667 , -0.023282 , -0.01426  , -0.031261 ,\n       -0.01445  , -0.019086 , -0.017776 , -0.018951 , -0.019193 ,\n       -0.024323 , -0.021811 , -0.028815 , -0.013792 , -0.025327 ,\n       -0.021204 , -0.014669 , -0.024293 , -0.016869 , -0.019921 ,\n       -0.017325 , -0.022056 , -0.019006 , -0.015761 , -0.019247 ,\n       -0.0069429, -0.014687 , -0.0067833, -0.017085 , -0.0094571,\n       -0.012888 , -0.013054 , -0.010311 , -0.014617 , -0.024905 ,\n       -0.012157 , -0.016666 , -0.010913 , -0.018628 , -0.018911 ,\n       -0.015936 , -0.033855 , -0.02337  , -0.016726 , -0.0141   ,\n       -0.020383 , -0.018098 , -0.019994 , -0.0097567, -0.026774 ,\n       -0.04357  , -0.017097 , -0.019039 , -0.019237 , -0.021547 ,\n       -0.023871 , -0.023247 , -0.018103 , -0.012049 , -0.021811 ,\n       -0.011139 , -0.017744 , -0.01123  , -0.014468 , -0.016065 ,\n       -0.016028 , -0.019477 , -0.014146 , -0.018802 , -0.00921  ,\n       -0.013541 , -0.014684 , -0.006355 , -0.009856 , -0.018237 ,\n       -0.019603 , -0.021236 , -0.011943 , -0.014418 , -0.020925 ,\n       -0.027817 , -0.017508 , -0.021509 , -0.00953  , -0.0152   ,\n       -0.021263 , -0.01017  , -0.0065271, -0.032973 , -0.013707 ,\n       -0.022841 , -0.014494 , -0.024446 , -0.023226 , -0.026063 ,\n       -0.028042 , -0.018809 , -0.040551 , -0.0066864, -0.030315 ,\n       -0.013748 , -0.023841 , -0.01677  , -0.025636 , -0.027406 ,\n       -0.024676 , -0.020194 , -0.023646 , -0.01435  , -0.013478 ,\n       -0.021398 , -0.0035517, -0.017634 , -0.017357 , -0.016666 ,\n       -0.008415 , -0.027327 , -0.015674 , -0.012839 , -0.012711 ,\n       -0.014075 , -0.016813 , -0.031901 , -0.01976  , -0.014225 ,\n       -0.019148 , -0.0035523, -0.022869 , -0.016685 , -0.01308  ,\n       -0.0087338, -0.017738 , -0.025458 , -0.01749  ])"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultsS = resultsS[~np.isnan(resultsS)]\n",
    "resultsS = resultsS[~np.isneginf(resultsS)]\n",
    "resultsS = resultsS[resultsS<0]\n",
    "resultsS"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              S1        ST   S1_conf   ST_conf\n",
      "rest    0.162316  0.905097  0.175084  0.079417\n",
      "amp     0.131016  0.897920  0.235596  0.145939\n",
      "freq    0.151285  0.891418  0.156806  0.121723\n",
      "pack    0.155999  0.696013  0.233689  0.103832\n",
      "visc    0.169021  0.830133  0.239078  0.118888\n",
      "rest_n  0.113672  0.921791  0.198509  0.111706\n",
      "amp_n   0.116332  0.776415  0.239109  0.107809\n",
      "freq_n  0.145264  0.796702  0.272230  0.122414\n"
     ]
    }
   ],
   "source": [
    "# Run sensitivity analysis for slopes\n",
    "SiS = SALib.analyze.fast.analyze(problem, resultsS[0:480], print_to_console=True)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SiS.plot()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 3)",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mValueError\u001B[0m                                Traceback (most recent call last)",
      "\u001B[1;32m~\\AppData\\Local\\Temp\\ipykernel_42360\\3645543763.py\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[0mtotal_Si\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0mfirst_Si\u001B[0m\u001B[1;33m,\u001B[0m \u001B[0msecond_Si\u001B[0m \u001B[1;33m=\u001B[0m \u001B[0mSiS\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mto_df\u001B[0m\u001B[1;33m(\u001B[0m\u001B[1;33m)\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m      2\u001B[0m \u001B[0mtotal_Si\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
      "\u001B[1;31mValueError\u001B[0m: too many values to unpack (expected 3)"
     ]
    }
   ],
   "source": [
    "total_Si, first_Si, second_Si = SiS.to_df()\n",
    "total_Si"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "outputs": [
    {
     "data": {
      "text/plain": "          S1   S1_conf\nx1  0.316832  0.065692\nx2  0.443763  0.055642\nx3  0.012203  0.052275",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>S1</th>\n      <th>S1_conf</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>x1</th>\n      <td>0.316832</td>\n      <td>0.065692</td>\n    </tr>\n    <tr>\n      <th>x2</th>\n      <td>0.443763</td>\n      <td>0.055642</td>\n    </tr>\n    <tr>\n      <th>x3</th>\n      <td>0.012203</td>\n      <td>0.052275</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_Si"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "outputs": [
    {
     "data": {
      "text/plain": "                S2   S2_conf\n(x1, x2)  0.009254  0.077990\n(x1, x3)  0.238172  0.101901\n(x2, x3) -0.004888  0.073313",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>S2</th>\n      <th>S2_conf</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>(x1, x2)</th>\n      <td>0.009254</td>\n      <td>0.077990</td>\n    </tr>\n    <tr>\n      <th>(x1, x3)</th>\n      <td>0.238172</td>\n      <td>0.101901</td>\n    </tr>\n    <tr>\n      <th>(x2, x3)</th>\n      <td>-0.004888</td>\n      <td>0.073313</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "second_Si"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "outputs": [
    {
     "data": {
      "text/plain": "[          ST   ST_conf\n x1  0.555860  0.084181\n x2  0.441898  0.039134\n x3  0.244675  0.021780,\n           S1   S1_conf\n x1  0.316832  0.065692\n x2  0.443763  0.055642\n x3  0.012203  0.052275,\n                 S2   S2_conf\n (x1, x2)  0.009254  0.077990\n (x1, x3)  0.238172  0.101901\n (x2, x3) -0.004888  0.073313]"
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Si.to_df()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "outputs": [
    {
     "data": {
      "text/plain": "{'S1': array([0.31683154, 0.44376306, 0.01220312]),\n 'S1_conf': array([0.06569207, 0.05564171, 0.05227495]),\n 'ST': array([0.55586009, 0.44189807, 0.24467539]),\n 'ST_conf': array([0.08418097, 0.03913363, 0.02178047]),\n 'S2': array([[        nan,  0.00925429,  0.23817211],\n        [        nan,         nan, -0.0048877 ],\n        [        nan,         nan,         nan]]),\n 'S2_conf': array([[       nan, 0.07798982, 0.10190104],\n        [       nan,        nan, 0.07331267],\n        [       nan,        nan,        nan]])}"
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Si"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
